电子科技
電子科技
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IT AGE
2012年
6期
136-139
,共4页
滚动轴承%最小二乘支持向量机%半监督学习%故障诊断
滾動軸承%最小二乘支持嚮量機%半鑑督學習%故障診斷
곤동축승%최소이승지지향량궤%반감독학습%고장진단
rolling bearing%LS_SVM%semi-supervised learning%fault identification
为提高滚动轴承故障诊断分类器的训练正确率，以及缩短训练时间，根据其训练集即含有标签样本，也含有无标签样本的特点，将LS_SVM与半监督学习相结合，充分利用训练集中的有效信息，给出一种基于SLS_SVM的滚动轴承故障诊断方法。将该方法与标准SVM和半监督学习SVM方法相比，其不但能提高训练正确率，也能缩短训练所需时间。通过诊断试验，验证了该算法的有效性以及高效性。
為提高滾動軸承故障診斷分類器的訓練正確率，以及縮短訓練時間，根據其訓練集即含有標籤樣本，也含有無標籤樣本的特點，將LS_SVM與半鑑督學習相結閤，充分利用訓練集中的有效信息，給齣一種基于SLS_SVM的滾動軸承故障診斷方法。將該方法與標準SVM和半鑑督學習SVM方法相比，其不但能提高訓練正確率，也能縮短訓練所需時間。通過診斷試驗，驗證瞭該算法的有效性以及高效性。
위제고곤동축승고장진단분류기적훈련정학솔，이급축단훈련시간，근거기훈련집즉함유표첨양본，야함유무표첨양본적특점，장LS_SVM여반감독학습상결합，충분이용훈련집중적유효신식，급출일충기우SLS_SVM적곤동축승고장진단방법。장해방법여표준SVM화반감독학습SVM방법상비，기불단능제고훈련정학솔，야능축단훈련소수시간。통과진단시험，험증료해산법적유효성이급고효성。
To improve the rate of correct training of the fault identification sorter of rolling bearing and shorten the training time, LS_SVM is combined with semi-supervised learning according to the fact that the training sets have both labeled and unlabeled examples. Full use is made of the effective information in the training sets and a novel SLS SVM based fanlt identification method for roiling bearing is proposed. A comparison of this method with the standard SVM and semi-supervised learning based on the SVM method shows that this method can not only improve the rate of correct training but also shorten the training time. The diagnostic tests show that it is an effective and efficient method.
